MATLAB Implementation of Particle Filter Algorithm

Particle filtering is a widely-used technique in the target tracking domain. This MATLAB implementation provides researchers with a practical tool to deepen their understanding of particle filter applications. The program enables trajectory prediction and target tracking through sequential Monte Carlo methods, where key functions handle state initialization, importance sampling, and resampling operations. Users can adjust parameters such as particle count, process noise, and measurement noise to optimize tracking accuracy. The code structure follows modular design principles, allowing customization through modified motion models (e.g., constant velocity or turning models) and observation models (e.g., range-bearing sensors). The implementation includes core algorithms for weight calculation and systematic resampling to mitigate particle degeneracy. With its extensible architecture, the program supports adaptations for various research scenarios, including multi-target tracking and nonlinear systems. The commented codebase facilitates understanding of algorithmic steps from prediction to update phases. This implementation serves as a valuable resource for researchers studying probabilistic filtering techniques and their real-world applications.